Tuesday, August 14, 2018

Robot bartenders are mostly novelties today. But a group of startups is hoping to bring automation to your neighborhood watering hole—and even your home bar

By Leigh Kamping-Carder of The WSJ. If workers are replaced by machines, economists call that structural unemployment. Below I have links to earlier posts on that as well as whether or not automation necessarily increases unemployment. One mentions "Flippy, a robot that turns the burgers and cleans the hot, greasy grill."

Excerpts:

"There’s no need to tip the mixologist at the Tipsy Robot, a
glittering bar in Las Vegas where automated arms handle all the shaking,
stirring, muddling and garnishing, making up to 120 cocktails an hour.

The silver-and-turquoise lounge, in the Miracle Mile Shops mall
on the Strip, has 28 counter-style seats, each equipped with a tablet,
facing a bar counter topped with two industrial-grade robotic arms.
Patrons can order signature and classic cocktails, or fill a virtual cup
with up to 14 ingredients of their choosing. Then the robotic arms go
to work, gathering ingredients from a kind of futuristic back-bar
automat; reaching up to a lattice of 120 liquor bottles; and tipping the
resulting cocktail into a plastic cup proffered by a mechanical
dispenser in the counter. Drinks take 60 to 90 seconds to make, and cost
$12 to $16, said Stephan Mornet, president of Robotic Innovations,
Tipsy Robot’s parent company."

"According to manufacturers, robot bartenders are money savers, cutting
down on spillage, eliminating employee theft and ensuring consistency.
Another selling point is their ability to collect data on drink orders
and, when users create profiles to save custom cocktails, on
demographics."

"The Smartender, another automated cocktail dispensing system, aims to
replace the back-of-house bartender who pours drinks for servers at
chain restaurants, casinos and sports stadiums. The system costs roughly
$30,000 including shipping, installation and training for employees, an
expense that Barry Fieldman of Smart Bar USA, the Las Vegas-based
manufacturer, said companies quickly recoup by reducing bar staff and
waste."

"Robot bartenders are unlikely to eliminate human bar jobs in the near
future, experts say. “The mistake to make is always to think that just
because a new piece of automation comes along that the total number of jobs is going to go down,”
said
Andrew McAfee,
a principal research scientist at MIT and co-founder of the
school’s Initiative on the Digital Economy. Crucially, a human needs to
be present to verify age and ensure that overly intoxicated patrons are
not served."

Monday, August 13, 2018

"In the mid-2000s, the nation experienced
a housing bubble. A combination of stupidity, negligence and
malfeasance led a bunch of Wall Street firms to make excessively risky
bets that the bubble would go on forever. Through mortgage-backed
securities and related instruments, they extended credit to home buyers
of dubious credit quality.

For a
while, this credit expansion fueled the bubble. But when the bubble
burst, these new homeowners defaulted in record numbers, and the Wall
Street firms headed toward insolvency. The whole financial system
teetered on the edge of collapse, leading to a deep recession.

Fortunately, policymakers came to the
rescue. Henry M. Paulson Jr., the secretary of the Treasury, persuaded
Congress to pass the Troubled Asset Relief Program, which he and his
successor, Timothy F. Geithner, used to recapitalize the banks. Ben S.
Bernanke, the chairman of the Federal Reserve, expanded the tools of
monetary policy to support the financial system and the economy more
broadly. Their bold steps saved us from another Great Depression.

In
this conventional narrative, Wall Street financiers are the villains
and Washington policymakers are the heroes. Certainly, the policymakers
have promoted this view. Mr. Bernanke even titled his memoir “The Courage to Act.”

Yet a new book, “The Fed and Lehman Brothers,” by Laurence M. Ball,
an economist at Johns Hopkins University, casts doubt on this
narrative. Mr. Ball (who is a friend of mine) does not excuse the
financiers from starting the trouble. But he draws attention to the
policymakers who, in his view, failed to do their jobs at a crucial
moment."

"A central bank can solve this liquidity problem by lending to a
financial institution experiencing a run. That is why the Federal
Reserve Act calls for an “elastic currency.”"

"When mortgage defaults started rising,
many financial institutions experienced a run on their short-term
liabilities. These liabilities were not traditional bank deposits but
rather repurchase agreements, called repos. But the forces at work were
much the same.In September 2008, the
financial giant Lehman Brothers found itself facing a liquidity crisis.
Yet the Fed, rather than acting as a lender of last resort, pushed
Lehman into bankruptcy."

"Mr. Ball argues that a careful look at
Lehman’s finances shows that it did have enough collateral. In addition,
he examines the historical record and finds no evidence that Fed
officials at the time were concerned about the insufficiency of
collateral.

The claim of inadequate
collateral arose weeks later when the full impact of the Lehman
bankruptcy became clear. It was, Mr. Ball suggests, an attempt to cover
up a policy blunder."

"the Dodd-Frank Act has increased restrictions on Fed lending, making it harder for the Fed to act as lender of last resort."

Sunday, August 12, 2018

The basic idea is that if you drive more safely, you have fewer accidents and insurance companies like that. Some of my students might recall one of the lessons from the supply and demand game. That was that one condition for markets to work optimally is that buyers and sellers have equal access to information. When they don't, markets won't work as well as they should.

For instance, in used car markets, the sellers know alot more about the product than the buyers. Economists have studied the problems this causes in the "market for lemons" research. If you want to sell your used car for $1,000, some people won't believe it is worth it. So they only offer maybe $800. If you believe your car is worth $1,000, you won't sell it. Then there are not enough sales in that market and the quantity is too low or sub-optimal. And many of the cars on the market are lemons.

But in insurance markets, buyers know more than the sellers. You know how risky you are but the insurance companies don't. Insurance companies want your premiums to reflect your risk. The riskier people need to pay higher premiums. If insurance companies can learn more about your driving habits, they can know better what to charge you.

"Here’s a look at what insurers are doing today — and what they might try next.

1. Tracking driving for discounts, rewards

Many major insurers now offer telematics,
technology that collects information about your driving behavior, in
exchange for discounts or rewards.

Progressive was first, having launched its
telematics-based program “Snapshot” in 2011. Customers who plug a device
into their cars’ diagnostic ports to allow the company to monitor their
driving can earn discounts. The technology — which tracks data like
acceleration, hard braking, time of day and how much you drive — is also
available in an app.

Other insurers that track driving behavior
reward safe drivers with cash back, freebies or a combination of rewards
and policy discounts. Often, drivers get a discount simply for opting
in. While many companies say that driving behavior is monitored solely
to determine discounts, Progressive might increase rates if your data
show unsafe behavior.

2. Setting prices based on your (actual) driving

Auto insurers’ use of demographic factors, such as age, gender and marital status, when setting rates isn’t exactly popular with drivers.

A start-up, Root Insurance, is trying a new
model: Pricing based on how you drive, which could save money for safe
drivers. The insurance, currently available in 19 states with plans for
five more, tracks driving behavior during a two- to six-week “test
drive” before giving you a quote.

The company still considers some demographic
factors, but it isn’t as interested in your personal details, says CEO
and co-founder Alex Timm. “There’s not really a ‘great driver’
demographic — we find them across the country, in all sorts of
situations,” Timm says.

Other companies are pricing coverage based on
how much you drive. In select states, MetroMile, Allstate and Esurance,
for example, offer policies where drivers pay a base rate, plus a
per-mile rate.

3. Evaluating driving to curb bad habits

Beyond offering discounts to customers who
opt into monitoring programs, insurers want to make you a better, safer
driver. Depending on the program, drivers may get immediate feedback
through in-app driving reports and scores, or even from devices that
beep when drivers brake hard or turn too sharply.

Insurers are also targeting distracted
driving, which was reported in 9% of fatal crashes in 2016, according to
the latest data from the National Highway Traffic Safety Administration.
Because crashes often result in claims, insurers hope to see a decrease
by monitoring cell phone use, a common driving distraction.

In addition to tracking how you drive, apps
from Root and AAA can tell if you’re using your phone while you drive,
which will have an impact on your rate. Arity, a subsidiary of Allstate,
is working to bring this capability to existing monitoring programs at
Allstate and Esurance."

The main idea seems to be using "economic fitness" to predict a country's future GDP (along with past GDP). Fitness means that a country has a wide variety of exports. If the rest of the world likes many of your products, that might mean your economy is "fit" or in good shape. Maybe that means that no matter what happens in the world economy, your country will usually have something other countries will want to buy. Excerpt:

"Applying physics to economics

The economy is a bit different from many physical models, though: there is no complete model of an economy. There isn’t even a good approximate model. Indeed, simple models that provide insight do not offer predictions. Instead, predictive models are statistical in nature. These make use of historical economic data to predict future economic data—essentially, the model looks for correlations between historical and recent data. These correlations are then used to take current economic data to predict future economic data. The model is then constructed from our understanding of how the measured data relates to economic activity.

The problem with this approach is that, if you don't have sufficient data, predictions quickly become inaccurate. To resolve this problem, we collect more information. That information allows new processes to be included in the model with the hope that this will yield increased accuracy over longer time intervals. And this certainly works: current models are better than older models.

To improve on predictive models about the economy, researchers took a counterintuitive approach. They reduced the number of parameters in their model to just two: economic fitness and gross domestic product, the idea being that if the economic fitness and GDP are measured at a given time, then the change in GDP can be predicted.

So what is the economic fitness? It is, in short, a measure of the complexity of a country’s exports. The idea is that exports represent the products from a country that are competitive with like products from the rest of the world. The larger the variety of exported products, the fitter an economy is. One advantage of it as a measure is that exports and imports are very carefully measured, because companies rely on that data to survive. And that data is collected and reported in a relatively standardized way. The researchers basically created a matrix that allows the variety of exports to be summed.

This number is then iteratively normalized with data from all other countries to come up with a self-consistent scale of economic fitness. Economic fitness drives changes in economic growth, which is accounted for in GDP.

Now, it is important to realize that no one really has a model (in the physical sense) of the link between economic fitness and GDP. But we do have statistical data that can be used to infer how the two are linked. We can estimate from the averages in the dataset how high the economic fitness of an economy has to be to support a given GDP and use that to determine if the GDP will increase or decrease.

The speed of the increase or decrease is estimated using a kind of force-response model. In other words, if the GDP is far away from that expected from the current economic fitness, there is a strong hidden economic drive to change the GDP. Hence, we can expect rapid economic growth (or contraction).

Predicting the past

This case is exemplified by China in 1995. China at that time had a low GDP but high economic fitness. As predicted by the model, China experienced 20 years of steep economic growth, with its GDP increasing remarkably. In a standard economic analysis, this seems extraordinary. But, the researchers argue that this is actually expected behavior: much like a stretched spring being released to jump back to its position.

The model also allows economic momentum to play a role. The speed at which economic fitness is changing also influences the change in GDP. The researchers found that using the trajectory of economic fitness to predict GDP leads to even more accurate results.

The researchers tested their model on historical data from 169 countries over three different five-year windows. They compared their GDP predictions with those produced by the international monetary fund (IMF) model and with the actual GDP data.

They found that their model was better than the IMF model, especially when they also took into account the trajectory of the economic fitness. Furthermore, a close analysis of how the IMF model and their dynamical model predictions differed showed that the sources of inaccuracy were different. That meant that combining the two models led to predictions that were even more accurate.

Another important factor is that there is a kind of self-similar behavior in trajectories. Even though the total size of the economy might be different, countries with similar ratios (I’m simplifying here) of economic fitness to GDP experienced similar trajectories. And a final point: the model also shows where predictability fails. Countries with a very low economic fitness are incredibly difficult to predict. This is true of both the IMF model and their model, but it highlights that the poorer you are, the more subject you are to the random buffeting of economic noise."

Friday, August 10, 2018

Gensowski revisits a data set from all schools in
California, grades 1-8, in 1921-1922, based on the students who scored
in the top 0.5 percent of the IQ distribution. At the time that meant
scores of 140 or higher. The data then cover how well these students,
856 men and 672 women, did through 1991. The students were rated on
their personality traits and behaviors, along lines similar to the “Big
Five” personality traits: openness to experience, conscientiousness,
extraversion, agreeableness and neuroticism.
One striking result is how much the trait of conscientiousness matters. Men who measure as one standard deviation higher
on conscientiousness earn on average an extra $567,000 over their
lifetimes, or 16.7 percent of average lifetime earnings.
Measuring as extroverted, again by one standard deviation higher than
average, is worth almost as much, $490,100. These returns tend to rise
the most for the most highly educated of the men.
For women, the magnitude of these effects is smaller…
It may surprise you to learn that more “agreeable” men earn
significantly less. Being one standard deviation higher on agreeableness
reduces lifetime earnings by about 8 percent, or $267,600.

There is much more at the link, and no I do not confuse causality
with correlation. See also my remarks on how this data set produces
some results at variance with the signaling theory of education. Here
is the original study."

Thursday, August 09, 2018

The table below shows the annual percentage change in per capita Real GDP and Real GDP since 2001. The base year is 2012. Data from the Commerce Department (Bureau of Economic Analysis). Then some timeline charts covering the years 1948-2017